The present invention relates generally to an alternating least square (ALS) recommendation system, and more particularly, but not by way of limitation, to a system for real-time recommendations in alternating least square.
Conventionally, content providers collect user ratings on content watches and attempt to predict user ratings on content that the users have not watched by factorizing a rating matrix and minimizing empirical loss using a batch-based algorithm (i.e. alternating least square (ALS)) to literally solve for the ratings. However, batch-based algorithms take a long time to finish or refresh for the sizable data set that is typical of collected user ratings such that the updated ratings of the content is out of date when the batch-based algorithm finishes an update.
That is, there is a technical challenge in the conventional techniques that the conventional techniques do not exploit the nature of matrix factorization such that users are provided with expedited and still accurate recommendation by using the batch-based algorithms.